AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	auc	0.5	0.93
	2_DecisionTree	Decision Tree	auc	0.529015	7.02
	3_Linear	Linear	auc	0.584224	5.73
the best	4_Default_Xgboost	Xgboost	auc	0.647255	52.3
	5_Default_NeuralNetwork	Neural Network	auc	0.562013	2.91
	6_Default_RandomForest	Random Forest	auc	0.595046	12.78
	Ensemble	Ensemble	auc	0.647255	0.45

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork

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Neural Network

• n_jobs: -1
• dense_1_size: 32
• dense_2_size: 16
• learning_rate: 0.05
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

auc

Training time

2.1 seconds

Metric details

	score	threshold
logloss	0.344652	nan
auc	0.562013	nan
f1	0.952559	0.654741
accuracy	0.909416	0.654741
precision	0.942529	0.985706
recall	1	0.654741
mcc	0.10233	0.964525

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.344652	nan
auc	0.562013	nan
f1	0.952559	0.654741
accuracy	0.909416	0.654741
precision	0.909416	0.654741
recall	1	0.654741
mcc	0	0.654741

Confusion matrix (at threshold=0.654741)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 1_Baseline

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Baseline Classifier (Baseline)

• n_jobs: -1
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

auc

Training time

0.3 seconds

Metric details

	score	threshold
logloss	0.303888	nan
auc	0.5	nan
f1	0.952559	0.818084
accuracy	0.909416	0.818084
precision	0.909416	0.818084
recall	1	0.818084
mcc	0	0.818084

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.303888	nan
auc	0.5	nan
f1	0.952559	0.818084
accuracy	0.909416	0.818084
precision	0.909416	0.818084
recall	1	0.818084
mcc	0	0.818084

Confusion matrix (at threshold=0.818084)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of Ensemble

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Ensemble structure

Model	Weight
4_Default_Xgboost	1

Metric details

	score	threshold
logloss	0.485151	nan
auc	0.647255	nan
f1	0.952559	0.405666
accuracy	0.909416	0.405666
precision	0.951705	0.654602
recall	1	0.405666
mcc	0.182698	0.652818

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.485151	nan
auc	0.647255	nan
f1	0.952559	0.405666
accuracy	0.909416	0.405666
precision	0.909416	0.405666
recall	1	0.405666
mcc	0	0.405666

Confusion matrix (at threshold=0.405666)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree

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Decision Tree

• n_jobs: -1
• criterion: gini
• max_depth: 3
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

auc

Training time

6.1 seconds

Metric details

	score	threshold
logloss	0.306519	nan
auc	0.529015	nan
f1	0.952559	0.465517
accuracy	0.909416	0.465517
precision	0.915254	0.863464
recall	1	0.465517
mcc	0.139959	0.710468

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.306519	nan
auc	0.529015	nan
f1	0.952559	0.465517
accuracy	0.909416	0.465517
precision	0.909416	0.465517
recall	1	0.465517
mcc	0	0.465517

Confusion matrix (at threshold=0.465517)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 6_Default_RandomForest

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Random Forest

• n_jobs: -1
• criterion: gini
• max_features: 0.9
• min_samples_split: 30
• max_depth: 4
• eval_metric_name: auc
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

auc

Training time

11.8 seconds

Metric details

	score	threshold
logloss	0.298998	nan
auc	0.595046	nan
f1	0.952559	0.500223
accuracy	0.909416	0.500223
precision	0.958333	0.949446
recall	1	0.500223
mcc	0.12452	0.883034

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.298998	nan
auc	0.595046	nan
f1	0.952559	0.500223
accuracy	0.909416	0.500223
precision	0.909416	0.500223
recall	1	0.500223
mcc	0	0.500223

Confusion matrix (at threshold=0.500223)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 4_Default_Xgboost

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Extreme Gradient Boosting (Xgboost)

• n_jobs: -1
• objective: binary:logistic
• eta: 0.075
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• eval_metric: auc
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

auc

Training time

51.3 seconds

Metric details

	score	threshold
logloss	0.485151	nan
auc	0.647255	nan
f1	0.952559	0.405666
accuracy	0.909416	0.405666
precision	0.951705	0.654602
recall	1	0.405666
mcc	0.182698	0.652818

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.485151	nan
auc	0.647255	nan
f1	0.952559	0.405666
accuracy	0.909416	0.405666
precision	0.909416	0.405666
recall	1	0.405666
mcc	0	0.405666

Confusion matrix (at threshold=0.405666)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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Summary of 3_Linear

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Logistic Regression (Linear)

• n_jobs: -1
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

auc

Training time

4.8 seconds

Metric details

	score	threshold
logloss	0.313337	nan
auc	0.584224	nan
f1	0.952559	0.14372
accuracy	0.909416	0.14372
precision	0.965812	0.967649
recall	1	0.14372
mcc	0.120534	0.813619

Metric details with threshold from accuracy metric

	score	threshold
logloss	0.313337	nan
auc	0.584224	nan
f1	0.952559	0.14372
accuracy	0.909416	0.14372
precision	0.909416	0.14372
recall	1	0.14372
mcc	0	0.14372

Confusion matrix (at threshold=0.14372)

	Predicted as C	Predicted as N
Labeled as C	0	76
Labeled as N	0	763

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

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